US7792343B2 - Elastic image registration functionality - Google Patents
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- US7792343B2 US7792343B2 US11/719,406 US71940605A US7792343B2 US 7792343 B2 US7792343 B2 US 7792343B2 US 71940605 A US71940605 A US 71940605A US 7792343 B2 US7792343 B2 US 7792343B2
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- 230000015654 memory Effects 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims description 19
- 230000009466 transformation Effects 0.000 claims description 17
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- 238000002059 diagnostic imaging Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims 1
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Images
Classifications
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- G06T3/153—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
Definitions
- the present invention relates to the diagnostic imaging arts. It finds particular application in conjunction with a CT imaging system and will be described with particular reference thereto. However, it is to be appreciated that the present invention is applicable to a wide range of diagnostic imaging modalities.
- the images which are formed at different instances by the same or different modalities, have to be registered by the means of scaling, rotating and the like to have the position and shape of the organs coincide.
- Rigid transformations are defined as geometrical transformations that preserve distances.
- the rigid transformations also preserve straightness of lines and all non-zero angles between straight lines.
- the rigid transformations are typically composed of translations and rotations.
- the image is modeled as an elastic body and the similarity between points or features in the two images act as external forces, which stretch the body.
- Elastic registration of images is used for a wide variety of clinical applications where images that have been acquired at different times, with different modalities, or for different patients need to be aligned with one another.
- the examples of images requiring elastic transformation include tumor diagnosis, surgery and treatment, where the images are typically taken at different modalities to show different aspects of the tumor, taken at different times to compare effects of pre-intervention and post-intervention images, or being matched with the anatomical atlases derived from cohorts studies.
- the present application contemplates a new and improved method and apparatus which overcomes the above-referenced problems and others.
- an apparatus for diagnostic imaging is disclosed.
- a first memory supplies a first diagnostic image.
- a second memory supplies a second diagnostic image.
- a registration routine automatically registers the first and second diagnostic images from the first and second image memories.
- a display concurrently displays at least a corresponding pair of 2D slices of the first and second registered diagnostic images.
- a method of diagnostic imaging is disclosed.
- a first diagnostic image of a selected region is supplied.
- a second diagnostic image of the selected region is supplied.
- the first and second diagnostic images are automatically registered.
- a corresponding pair of 2D slices of the first and second registered diagnostic images is concurrently displayed. At least one of the currently displayed 2D slices corresponding to one of the first and second registered diagnostic images is manually transformed.
- One advantage of the present invention resides in computational efficiency.
- Another advantage resides in efficiency of correction of misregistered images.
- Another advantage resides in real time display of corrected images.
- the invention may take form in various components and arrangements of components, and in various steps and arrangements of steps.
- the drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
- FIG. 1 is a diagrammatic illustration of a diagnostic imaging system
- FIGS. 2-3 are graphical representations of a Gaussian pull tool
- FIGS. 4-5 are graphical representations of a sphere push tool.
- a subject is positioned in a diagnostic imager 10 , such as a CT scanner, for a follow-up examination.
- the generated data is reconstructed by a reconstruction processor 12 and stored in a 3D volumetric image memory 14 (image A).
- image A 3D volumetric image memory 14
- image enhancement operations as are known in the art, are preferably performed.
- Image data from the hospital archive or from another storage medium 16 of the same region of the same subject is retrieved and stored in an archived 3D volumetric image memory 18 (image B).
- image B an archived 3D volumetric image memory 18
- both the current and archive 3D image memories 14 , 18 may be parts of a common storage medium.
- a segmenting means or process 30 preferably automatically, outlines the boundaries of one or more selected anatomical structures of a region of interest of the subject. In this manner, the surface of the same selected structure(s) is defined in both images A and B.
- a pre-determined 3D model of the region of interest or an organ to be segmented in the diagnostic image is selected.
- the model represents an anatomical organ such as a bladder or femur, but it may also represent a structure such as a target volume for radiotherapy.
- the model is used to aid automated image segmentation by providing knowledge of the organ shape as an initial starting point for the automated segmenting process 30 .
- An aligning means or process 38 registers the images A, B for a concurrent display on one or more monitors or displays 40 . More specifically, an affine transform means 42 performs a first step of the aligning process 38 and approximately aligns images A, B by determining a misalignment between point landmarks in the current and archived 3D images A, B. Specifically, the affine transform means 42 searches for the most distinguished anatomical features in the segmented areas of the images A, B such as characteristic portions of the body around the region of interest, e.g. unique locations on the skull or the vertebrae, and determines an affine transform between the misaligned landmarks.
- the affine transform means 42 searches for the fiducials or imagable markers that have been affixed to the subject closely adjacent the region of interest. When such common points are determined, the affine transform means 42 applies appropriate algorithms, known in the art, to align the images A, B. In one embodiment, the affine transform means 42 determines nine rotational components about three orthogonal axes and three translational components along the three axes that define the registration error. Optionally, a scaling parameter can also be determined.
- An elastic transform means 44 performs a second step of the aligning process 38 by a use of a point based elastic registration. More specifically, the elastic transform means 44 determines misalignment between the landmarks caused by non-rigid motions and the like and applies a closed-form elastic transformation to the misaligned landmarks. More specifically, the closed-form Gaussian elastic transformation uses the Gaussian-shaped forces centered at the positions of the landmarks to elastically deform the images A, B in a way that the prescribed landmark correspondences (displacements) are preserved.
- the elastic transform means 44 preferably applies an elastic transform operator:
- the images A, B, aligned by the affine transform means 42 and the elastic transform means 44 , are stored in an aligned images memory 46 .
- a video processor 50 formats the aligned images A, B for display on the monitor 40 of a workstation 52 such that corresponding first and second sets of 2D slices of the aligned images A, B are displayed concurrently.
- a user manipulates the displayed slices using the workstation 52 which includes a CPU processor or hardware means 54 and a software means 56 for carrying out the necessary image processing functions and operations.
- the workstation 52 preferably includes one or more input devices 58 by the use of which the user can selectively control the workstation 52 and/or the scanner 10 .
- the user initiates an alignment correction via an image reformatting means 70 which includes a set of manual local tools 72 .
- the image reformatting means 70 allows the user to manipulate local regions of the 2D image A to match the 2D image B (or vice versa) more accurately or in accordance with; user's preferences.
- the video processor 50 includes an orthoviewer which retrieves and displays first and second sets of 2D orthogonal slices. More specifically, the three 2D orthogonal views through a selected crossing point in the image A are displayed; along with the same three orthogonal views through the corresponding crossing point in the image B. The crossing point in either image is used to index the displayed 2D slices in both views concurrently.
- the user corrects alignment only in the first and second sets of slices that are currently displayed on the monitor 40 . Since only maximum three 2D slices are updated per correction, the currently available hardware platforms carry out image reformatting in a substantially real-time domain.
- the local tools 72 comprise three main functions: selection of the local region (vertices) to be modified, the method by which the vertices are transformed, and the translation of the mouse motion into parameters defining the transformation.
- one example of the local tool is a Gaussian pull tool 74 which deforms a 2D image A surface by pulling a selected surface by a Gaussian weighted distance of the mouse motion d.
- the point that is at the initial position 76 of the mouse 58 moves into position 78 the same distance d as the mouse motion d.
- Surface points that lie farther away from the mouse 58 move a shorter distance based on a Gaussian function scaling of the mouse motion.
- the tool 74 is controlled by a single Gaussian radius which defines the width of the Gaussian spread.
- the Gaussian tool 74 is controlled by separate x- and y-Gaussian radii which allow for the x-radius to be used in the plane of motion of the mouse, and the y-radius to be used orthogonally to the drawing plane.
- the Gaussian tool 74 is controlled by a function, e.g. triangle, parabola, etc., that smoothly transitions from 1 to 0 with the appropriate set of parameters to accomplish a transformation of the selected vertices.
- the Gaussian pull tool 74 pulls a Gaussian shaped distortion (or other functional shape the smoothly transitions from 1 to 0) but derives the distance that the distortion is pulled from the distance of the mouse position from the 2D image plane.
- the 2D surface is pulled directly to the mouse position enabling smooth drawing, rather than having to click up and down on the mouse to grab and stretch the organ.
- the Gaussian is applied to the image in the displayed slices, it can affect the other two orthogonal slices if it is applied near the crossing point.
- the Gaussian deformation also affects neighboring parallel slices. However, the neighboring slices are not modified at the present time. Rather, the deformation parameters in other planes are saved and the modification to the surface in each neighboring plane is made when and if such neighboring plane is called up for display.
- a push tool 80 such as a push sphere which conforms the segment surface portions a specified radius R around the mouse location 82 to the surface of a sphere or circle in the displayed plane.
- the 2D image A is pushed either inward or outward depending on the location of the surface with respect to the mouse location 82 .
- the illustrated sphere tool 80 is controlled by a single sphere radius parameter. In this way, the surface is deformed analogous to pressing a spherical tool of the selected radius against a soft clay surface.
- other surfaces of predetermined shapes such as ellipses are also contemplated.
- the reformatted slices are stored in a data memory 90 .
- the reformatted slices are stored in a cache-conscious way to accelerate the reinspection if so requested.
- the user controls a stepping means 92 which causes the video processor 50 to withdraw and display corresponding 2D slices from the data memory 90 on the monitor 40 .
- the corresponding manually transformed regions are preferably calculated and updated on the fly.
- an updating means 94 pre-computes slices adjacent to the currently displayed corrected slices without waiting for the user interaction. If the user elects, the reformatted slices to become part of the permanent record for storage in the electronic archives, the 3D image is automatically updated.
- the update of the 3D image in accordance with manual transformations of the 2D slices is done at the session closing or at the dead time. For example, the user activates a “save” option (not shown) on the monitor 40 which action initiates saving and updating of the 3D image.
- the corresponding slices of the images A, B are superimposed.
- the user uses the manual tools 72 to deform surfaces in one or both images A, B to align the image A, B with one another.
- this technique is also applicable to magnetic resonance images, PET images, SPECT images, and other three-dimensional diagnostic images.
- the images being registered may be from mixed modalities.
- a CT image can be registered using this technique with a PET image.
- this technique is applicable to studies of a variety of organs such as the colon, the liver, and other non-rigid organs.
- this technique is also applicable to rigid portions of the body such as the head.
Abstract
Description
- pi are the positions of the ith landmark in the source image,
- G (x−pi) denotes the basis function,
- N is the overall number of landmarks in the image,
- ci are coefficients which are computed by solving a system of linear equations that results from the interpolation constraints and the displacements of corresponding landmarks.
Claims (16)
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Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
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US20080186378A1 (en) * | 2007-02-06 | 2008-08-07 | Feimo Shen | Method and apparatus for guiding towards targets during motion |
US20080205719A1 (en) * | 2005-06-15 | 2008-08-28 | Koninklijke Philips Electronics, N.V. | Method of Model-Based Elastic Image Registration For Comparing a First and a Second Image |
US20090043172A1 (en) * | 2006-06-02 | 2009-02-12 | Koninklijke Philips Electronics N. V. | Multi-modal imaging system and workstation with support for structured hypothesis testing |
US20110075946A1 (en) * | 2005-08-01 | 2011-03-31 | Buckland Eric L | Methods, Systems and Computer Program Products for Analyzing Three Dimensional Data Sets Obtained from a Sample |
US20110103551A1 (en) * | 2004-08-13 | 2011-05-05 | Koninklijke Philips Electronics N.V. | Radiotherapeutic Treatment Plan Adaptation |
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Families Citing this family (20)
Publication number | Priority date | Publication date | Assignee | Title |
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US20070280556A1 (en) * | 2006-06-02 | 2007-12-06 | General Electric Company | System and method for geometry driven registration |
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Citations (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5633951A (en) * | 1992-12-18 | 1997-05-27 | North America Philips Corporation | Registration of volumetric images which are relatively elastically deformed by matching surfaces |
US5682886A (en) | 1995-12-26 | 1997-11-04 | Musculographics Inc | Computer-assisted surgical system |
US5970182A (en) | 1995-11-15 | 1999-10-19 | Focus Imaging, S. A. | Registration process for myocardial images |
US20020097901A1 (en) * | 1998-02-23 | 2002-07-25 | University Of Chicago | Method and system for the automated temporal subtraction of medical images |
US6539127B1 (en) * | 1998-07-28 | 2003-03-25 | Inria Institut National De Recherche | Electronic device for automatic registration of images |
US20030063788A1 (en) * | 2001-10-03 | 2003-04-03 | Eastman Kodak Company | Method for registering images in a radiography application |
US20030128890A1 (en) | 2001-12-22 | 2003-07-10 | Peter Roesch | Method of forming different images of an object to be examined |
US20030181808A1 (en) | 1999-03-15 | 2003-09-25 | Mckinnon Graeme C. | Integrated multi-modality imaging system and method |
US20030233039A1 (en) | 2002-06-12 | 2003-12-18 | Lingxiong Shao | Physiological model based non-rigid image registration |
US6728424B1 (en) * | 2000-09-15 | 2004-04-27 | Koninklijke Philips Electronics, N.V. | Imaging registration system and method using likelihood maximization |
US6754374B1 (en) | 1998-12-16 | 2004-06-22 | Surgical Navigation Technologies, Inc. | Method and apparatus for processing images with regions representing target objects |
US20050013471A1 (en) * | 2003-07-18 | 2005-01-20 | R2 Technology, Inc., A Delaware Corporation | Model-based grayscale registration of medical images |
US7106891B2 (en) * | 2001-10-15 | 2006-09-12 | Insightful Corporation | System and method for determining convergence of image set registration |
US20060204064A1 (en) * | 2003-07-30 | 2006-09-14 | Ori Hay | Automatic registration of intra-modality medical volume images using affine transformation |
Family Cites Families (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS6357030A (en) * | 1986-08-27 | 1988-03-11 | 株式会社東芝 | Medical image diagnostic apparatus |
JP2955912B2 (en) * | 1993-02-18 | 1999-10-04 | 住友重機械工業株式会社 | Method for estimating the internal structure of irregular shaped objects |
JP3570576B2 (en) * | 1995-06-19 | 2004-09-29 | 株式会社日立製作所 | 3D image synthesis and display device compatible with multi-modality |
JP4018300B2 (en) * | 1999-09-27 | 2007-12-05 | ザイオソフト株式会社 | Image processing device |
JP4609960B2 (en) * | 1999-11-12 | 2011-01-12 | 株式会社日立メディコ | Image processing device |
JP2002109538A (en) * | 2000-10-03 | 2002-04-12 | Fuji Photo Film Co Ltd | Method and device for aligning image |
JP3954318B2 (en) * | 2001-03-09 | 2007-08-08 | 独立行政法人科学技術振興機構 | 3D model deformation system |
US7346381B2 (en) * | 2002-11-01 | 2008-03-18 | Ge Medical Systems Global Technology Company Llc | Method and apparatus for medical intervention procedure planning |
WO2004047025A2 (en) * | 2002-11-18 | 2004-06-03 | Koninklijke Philips Electronics N.V. | Method and device for image registration |
JP4560643B2 (en) * | 2003-06-17 | 2010-10-13 | 株式会社Aze | Ventilation distribution measurement method using respiratory CT images |
-
2005
- 2005-11-04 US US11/719,406 patent/US7792343B2/en active Active
- 2005-11-04 CN CN2005800393993A patent/CN101061508B/en active Active
- 2005-11-04 WO PCT/IB2005/053628 patent/WO2006054191A1/en active Application Filing
- 2005-11-04 JP JP2007540775A patent/JP5676840B2/en active Active
- 2005-11-04 EP EP05819757.5A patent/EP1815432B1/en active Active
Patent Citations (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5633951A (en) * | 1992-12-18 | 1997-05-27 | North America Philips Corporation | Registration of volumetric images which are relatively elastically deformed by matching surfaces |
US5970182A (en) | 1995-11-15 | 1999-10-19 | Focus Imaging, S. A. | Registration process for myocardial images |
US5682886A (en) | 1995-12-26 | 1997-11-04 | Musculographics Inc | Computer-assisted surgical system |
US20020097901A1 (en) * | 1998-02-23 | 2002-07-25 | University Of Chicago | Method and system for the automated temporal subtraction of medical images |
US6539127B1 (en) * | 1998-07-28 | 2003-03-25 | Inria Institut National De Recherche | Electronic device for automatic registration of images |
US6754374B1 (en) | 1998-12-16 | 2004-06-22 | Surgical Navigation Technologies, Inc. | Method and apparatus for processing images with regions representing target objects |
US20030181808A1 (en) | 1999-03-15 | 2003-09-25 | Mckinnon Graeme C. | Integrated multi-modality imaging system and method |
US6728424B1 (en) * | 2000-09-15 | 2004-04-27 | Koninklijke Philips Electronics, N.V. | Imaging registration system and method using likelihood maximization |
US20030063788A1 (en) * | 2001-10-03 | 2003-04-03 | Eastman Kodak Company | Method for registering images in a radiography application |
US7106891B2 (en) * | 2001-10-15 | 2006-09-12 | Insightful Corporation | System and method for determining convergence of image set registration |
US20030128890A1 (en) | 2001-12-22 | 2003-07-10 | Peter Roesch | Method of forming different images of an object to be examined |
US20030233039A1 (en) | 2002-06-12 | 2003-12-18 | Lingxiong Shao | Physiological model based non-rigid image registration |
US20050013471A1 (en) * | 2003-07-18 | 2005-01-20 | R2 Technology, Inc., A Delaware Corporation | Model-based grayscale registration of medical images |
US20060204064A1 (en) * | 2003-07-30 | 2006-09-14 | Ori Hay | Automatic registration of intra-modality medical volume images using affine transformation |
US7627158B2 (en) * | 2003-07-30 | 2009-12-01 | Koninklijke Philips Electronics N.V. | Automatic registration of intra-modality medical volume images using affine transformation |
Non-Patent Citations (11)
Title |
---|
Betke, M., et al.; Automatic 3D Registration of Lung Surfaces in Computed Tomography Scans; 2001; Lecture Notes in Computer Science; vol. 2208; pp. 725-733. |
Devabhaktuni, S., et al.; Elastic Image Registration of Diagnostic Brain MR and Limited View Intra Operative MR Using Mutual Information; 2003; IEEE; pp. 104-105. |
Hutton, B.-"Image registration: an essential tool for nuclear medicine"-European Journal of Nuclear Medicine, vol. 29, No. 4, Apr. 2002. * |
Hutton, B.—"Image registration: an essential tool for nuclear medicine"—European Journal of Nuclear Medicine, vol. 29, No. 4, Apr. 2002. * |
Kohlrausch, J., et al.; A New Class of Elastic Body Splines for Nonrigid Registration of Medical Images; Universitat Hamburg, 2001. |
MRIcro Tutorial; Nottingham Phychology; 2000; pp. 1-17. |
Periaswamy, S., et al.; Differential Elastic Image Registration; TR2001-413; Dartmouth College, Computer Science. |
Rajasekar, D., et al.; A Graphical User Interface for Automatic Image Registration Software Designed for Radiotherapy Treatment Planning; 2004; Medical Dosimetry; 29(4)239-246. |
Rohling, R. N., et al.; Automatic Registration of 3-D Ultrasound Images; 1998; Ultrasound in Med. & Biol.; 24(6) 841-854. |
Thompson, P., et al.; Elastic Image Registration and Pathology Detection; A book chapter for: Handbook of Medical Image Processing; Academic Press, 1999. |
Zuk, T. D., et al.; A Comparison of Manual and Automatic Methods for Registering Scans of the Head; 1996; IEEE Trans. on Medical Imaging; 15(5)732-744. |
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WO2006054191A1 (en) | 2006-05-26 |
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